41 research outputs found
Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
In this paper, we design and evaluate a convolutional autoencoder that
perturbs an input face image to impart privacy to a subject. Specifically, the
proposed autoencoder transforms an input face image such that the transformed
image can be successfully used for face recognition but not for gender
classification. In order to train this autoencoder, we propose a novel training
scheme, referred to as semi-adversarial training in this work. The training is
facilitated by attaching a semi-adversarial module consisting of a pseudo
gender classifier and a pseudo face matcher to the autoencoder. The objective
function utilized for training this network has three terms: one to ensure that
the perturbed image is a realistic face image; another to ensure that the
gender attributes of the face are confounded; and a third to ensure that
biometric recognition performance due to the perturbed image is not impacted.
Extensive experiments confirm the efficacy of the proposed architecture in
extending gender privacy to face images
Face Cartoonisation For Various Poses Using StyleGAN
This paper presents an innovative approach to achieve face cartoonisation
while preserving the original identity and accommodating various poses. Unlike
previous methods in this field that relied on conditional-GANs, which posed
challenges related to dataset requirements and pose training, our approach
leverages the expressive latent space of StyleGAN. We achieve this by
introducing an encoder that captures both pose and identity information from
images and generates a corresponding embedding within the StyleGAN latent
space. By subsequently passing this embedding through a pre-trained generator,
we obtain the desired cartoonised output. While many other approaches based on
StyleGAN necessitate a dedicated and fine-tuned StyleGAN model, our method
stands out by utilizing an already-trained StyleGAN designed to produce
realistic facial images. We show by extensive experimentation how our encoder
adapts the StyleGAN output to better preserve identity when the objective is
cartoonisation